Book description
Mine valuable insights from your data using popular tools and techniques in R
About This Book
 Understand the basics of data mining and why R is a perfect tool for it.
 Manipulate your data using popular R packages such as ggplot2, dplyr, and so on to gather valuable business insights from it.
 Apply effective data mining models to perform regression and classification tasks.
Who This Book Is For
If you are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a practical manner, this is the book for you. No previous experience of data mining is required.
What You Will Learn
 Master relevant packages such as dplyr, ggplot2 and so on for data mining
 Learn how to effectively organize a data mining project through the CRISPDM methodology
 Implement data cleaning and validation tasks to get your data ready for data mining activities
 Execute Exploratory Data Analysis both the numerical and the graphical way
 Develop simple and multiple regression models along with logistic regression
 Apply basic ensemble learning techniques to join together results from different data mining models
 Perform text mining analysis from unstructured pdf files and textual data
 Produce reports to effectively communicate objectives, methods, and insights of your analyses
In Detail
R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R.
It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques.
While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data.
Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.
Style and approach
This book takes a practical, stepbystep approach to explain the concepts of data mining. Practical usecases involving realworld datasets are used throughout the book to clearly explain theoretical concepts.
Publisher resources
Table of contents
 Preface
 Why to Choose R for Your Data Mining and Where to Start
 A First Primer on Data Mining Analysing Your Bank Account Data
 The Data Mining Process  CRISPDM Methodology
 Keeping the House Clean – The Data Mining Architecture
 How to Address a Data Mining Problem – Data Cleaning and Validation
 Looking into Your Data Eyes – Exploratory Data Analysis
 Our First Guess – a Linear Regression
 A Gentle Introduction to Model Performance Evaluation
 Don't Give up – Power up Your Regression Including Multiple Variables

A Different Outlook to Problems with Classification Models
 What is classification and why do we need it?
 Logistic regression
 Support vector machines
 References
 Summary

The Final Clash – Random Forests and Ensemble Learning
 Random forest
 Ensemble learning
 Applying estimated models on new data
 A more structured approach to predictive analytics
 Applying the majority vote ensemble technique on predicted data
 Further references
 Summary
 Looking for the Culprit – Text Data Mining with R

Sharing Your Stories with Your Stakeholders through R Markdown
 Principles of a good data mining report
 Set up an rmarkdown report
 Develop an R markdown report in RStudio
 Rendering and sharing an R markdown report
 Further references
 Summary
 Epilogue
 Dealing with Dates, Relative Paths and Functions
Product information
 Title: R Data Mining
 Author(s):
 Release date: November 2017
 Publisher(s): Packt Publishing
 ISBN: 9781787124462
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